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1 ABSTRACT INTERACTIVE LEARNING ONLINE AT PUBLIC UNIVERSITIES: EVIDENCE FROM A SIX-CAMPUS RANDOMIZED TRIAL William G. Bowen, Ithaka S+R Matthew M. Chingos, Brookings Institution and Ithaka S+R Kelly A. Lack, Ithaka S+R Thomas I. Nygren, Ithaka S+R Online instruction is quickly gaining in importance in U.S. higher education, but little rigorous evidence exists as to its effect on student learning. We measure the effect on learning outcomes of a prototypical interactive learning online statistics course by randomly assigning students on six public university campuses to take the course in a hybrid format (with machine-guided instruction accompanied by one hour of face-to-face instruction each week) or a traditional format (as it is usually offered by their campus, typically with about three hours of face-to-face instruction each week). We find that learning outcomes are essentially the same that students in the hybrid format pay no price for this mode of instruction in terms of pass rates, final exam scores, and performance on a standardized assessment of statistical literacy. We also conduct speculative cost simulations and find that adopting hybrid models of instruction in large introductory courses has the potential to significantly reduce instructor compensation costs in the long run. INTRODUCTION The American system of higher education is under increasing pressure to produce more graduates, and to do so with fewer resources. There is growing concern that the U.S. is losing its competitive edge in an increasingly knowledge-driven world, as many other countries make much more rapid progress than the U.S. in educating larger numbers of their citizens (Chingos, 2012). At the same time, higher education, especially in the public sector, is increasingly short of resources. Over the 10-year period from 2002 to 2012, state appropriations to their public universities decreased by 29 percent, on a per-student basis, from $8,352 to $5,906 in inflationadjusted dollars. At the same time, enrollment increased by 28 percent, from 9.0 to 11.5 million full-time equivalent students (State Higher Education Executive Officers, 2013). Falling support for higher education by state governments has been largely offset by increases in tuition revenue. But the days of higher tuition as an escape valve may be coming to 1

2 an end, with growing concern about tuition levels and increasing resentment among students and their families that is having political reverberations. President Obama, in his 2012 State of the Union address and in subsequent speeches, has decried rising tuitions, called upon colleges and universities to control costs, and proposed to withhold access to some Federal programs for colleges and universities that did not address affordability issues or meet completion tests (Obama, 2012). Today, a variety of higher education institutions must confront the challenge of how to manage costs in the face of tighter funding. In recent years, while the proportion of education spending drawn from tuition revenues rose across all institutions, increases in tuition often outpaced increases in education and related spending (i.e. spending on instruction, student services, and some support and maintenance costs related to these functions), calling into question the sustainability of the current funding model. 1 Moreover, a recent survey of provosts and chief academic officers found that very few of these administrators (and especially those at both public and private doctoral universities) gave their institutions high marks on effectiveness at controlling costs (Jaschik, 2012). A fundamental source of the problem is the cost disease, based on the labor-intensive nature of education with its attendant lack of opportunities for gains in productivity (Baumol and Bowen, 1966). But the time may finally be at hand when advances in information technology will permit, under the right circumstances, increases in productivity that can be translated into 1 According to the College Board (2011), tuition at public two-year universities in the academic year increased, on average, by 8.7 percent relative to the previous academic year, a period during which tuition at public four-year institutions increased, on average, by 8.3 percent for in-state students and by 5.7 percent for out-of-state students. In keeping with the trend over the previous four years, students attending private institutions experienced smaller percentage increases (4.5 percent for private not-for-profit four-year institutions and 3.2 percent for private for-profit institutions). 2

3 reductions in the cost of instruction. 2 Greater and smarter use of technology in teaching is widely seen as a promising way of controlling costs while also improving access. The exploding growth in online learning is often cited as evidence that, at last, technology may offer pathways to progress. 3 Online learning is seen by a growing number of people as a way of breaking free of century-old rigidities in educational systems that we have inherited (see, e.g., Christensen and Eyring, 2011). There are, however, also concerns that at least some kinds of online learning are low quality and that online learning in general de-personalizes education. In this regard, it is critically important to recognize issues of nomenclature: online learning is hardly one thing. It comes in a dizzying variety of flavors, ranging from simply videotaping lectures and posting them for any-time access, to uploading materials such as syllabi, homework assignments, and tests to the Internet, all the way to highly sophisticated interactive learning systems that use cognitive tutors and take advantage of multiple feedback loops. The varieties of online learning can be used to teach many kinds of subjects to different populations in diverse institutional settings. In important respects, the online learning marketplace reflects the diversity of American higher education itself. The rapid growth in the adoption of online learning has been accompanied by an unfortunate lack of rigorous efforts to evaluate these new instructional models, in terms of their 2 Baumol and Bowen (1966) argue that in fields such as the performing arts and education there is less opportunity than in other fields to improve productivity (by, for example, substituting capital for labor). Consequently, unit labor costs will rise inexorably as these sectors have to compete for labor with other sectors in which productivity gains are easier to come by, and the relative costs of labor-intensive activities such as chamber music and teaching will therefore continue to rise. Bowen (2001) argues that, for a number of years, advances in information technology have in fact increased productivity, but these increases have been enjoyed primarily in the form of more output (especially in research) and have generally led to higher, not lower, total costs. 3 A January 2013 report by the Babson Survey Research Group (Allen and Seaman, 2013) shows that between fall 2002 and fall 2011, enrollments in online courses increased much more quickly than total enrollments in higher education. During this time period, the number of online course enrollments grew from 1.6 million to 6.7 million, amounting to a compound annual rate of 17 percent (compared with a rate of three percent for course enrollments in general). More than three of every 10 students in higher education now take at least one course online. 3

4 effects on both quality and costs. There have been literally thousands of studies of online learning, but the vast majority do not meet minimal standards of evidence (U.S. Department of Education, 2010) and only a handful involve semester-long courses in higher education (Jaggars and Bailey, 2010). Fewer still look directly at the teaching of large introductory courses in basic fields at major public universities, where the great majority of undergraduate students pursue either associate or baccalaureate degrees. And barely any studies use random assignment with sizeable student populations, leaving open the question of whether the results simply reflect student selection into online courses. An important exception is Figlio, Rush, and Yin s (Forthcoming) randomized experiment in which they assigned students in an introductory microeconomics course at a selective research university to attend live lectures or watch online videos of the same lectures. They found no statistically significant differences in overall student achievement between the two formats, but did find evidence of negative online video effects among lower-achieving students, Hispanic students, and male students. There are several important differences between Figlio, Rush, and Yin s study and the present study that we return to below, but the most important distinction, in our view, is between the relatively primitive form of online instruction (videotaped lectures) evaluated in their study and the more sophisticated, interactive course examined in the present study (which we describe in more detail below). Other studies comparing online and face-to-face formats involve still other variations of online or hybrid learning. The existing research, though subject to many caveats about quality and relevance, does not suggest that online or hybrid learning is more or less effective, on average, than traditional face-to-face learning (Lack, 2013). Not only do the types of online or hybrid learning involved in the literature vary considerably, but so do the kinds of outcomes 4

5 measured, which range from homework assignment scores and project grades, to exam scores, final course grades, and completion and withdrawal rates. Many studies involve multiple measures of student performance, and within a single study, there are few instances in which one group outperforms the other group on all performance measures evaluated. The lack of consistency in findings may result from the wide variety of types of online learning studied and of research methodologies used, ranging from purely observational research to quasiexperimental studies to, in relatively few instances, randomized studies. Moreover, the variety in both research methodology and in forms of online learning, and the absence of a definitive pattern of online students consistently outperforming their face-to-face-format peers (or vice versa), render it difficult to reach any conclusions about what particular features of online courses are most or least conducive to enhancing student learning. This study fills a significant gap in the literature about the relative effectiveness of different learning formats by providing the first evidence from randomized experiments of hybrid instruction conducted at a significant scale across multiple public university campuses. Given the pressing need for institutions to use limited resources as effectively as possible, the research reported here is concerned with educational costs as well, which have also received limited attention in prior research related to the effectiveness of online instruction. We first describe the results of an experimental evaluation of a prototype interactive learning online course delivered in a hybrid mode (with some face-to-face instruction) on public university campuses in the Northeast and Mid-Atlantic. This section which contains the results of the main part of this study is followed by a briefer discussion of the potential cost savings that can conceivably be achieved by the adoption of hybrid-format online learning systems. We explain why we favor using a cost simulation approach to estimate potential savings, but we 5

6 relegate to Appendix B the highly provisional results we obtained by employing one set of assumptions in a cost simulation model. 4 RESEARCH DESIGN Our research is directed at assessing the educational outcomes associated with what we term interactive learning online or ILO. By ILO we refer to highly sophisticated, interactive online courses in which machine-guided instruction can substitute for some (though not usually all) traditional, face-to-face instruction. Course systems of this type take advantage of data collected from large numbers of students in order to offer each student customized instruction, as well as allow instructors to track students progress in detail so that they can provide their students with more targeted and effective guidance. We worked with seven instances of a prototype ILO statistics course developed at Carnegie Mellon University (CMU). 5 The CMU statistics course includes textual explanations of concepts and an inventory of worked examples and practice problems, some of which require the students to manipulate data for themselves using a statistical software package. Both the statistics course and other courses in the OLI suite were originally intended to be comprehensive enough to allow students to learn the material independently without the guidance of an instructor; since it was developed, however, the statistics course has been used at a variety of higher education institutions, sometimes in a hybrid mode. 6 Among the main strengths of the CMU statistics course is its ability to embed interactive assessments into each instructional activity, and its three key feedback loops: system to student, as the student answers questions; 4 All appendices are available at the end of this article as it appears in JPAM online. Go to the publisher s website and use the search engine to locate the article at 5 The CMU statistics course can be accessed at 6 Walsh (2011) describes the history of the development of this course, which was financed largely by the William and Flora Hewlett Foundation over a number of years. 6

7 system to teacher, to inform student-instructor interactions; and system to course developer, to identify aspects of the course that can be improved. In addition to offering assessments to measure how well students understand a particular concept, the CMU course also asks students to complete self-assessments in order to give the instructor and learning scientists a sense of how well students think they understand the concept. However, although instructors can delete and re-order modules, CMU s platform does not offer much opportunity for customization, nor is the course adaptive in terms of redirecting students to extra practice sessions or additional reading if their incorrect answers indicate that they do not understand a concept and need more help. Thus, although the CMU statistics course is certainly impressive, we refer to it as a prototype because we believe it is an early representative of what will likely be a wave of even more sophisticated systems in the not-distant future. Although the CMU course can be delivered in a fully online environment, in this study it was used in a hybrid mode in which most of the instruction was delivered through the interactive online materials, but the online instruction was supplemented by a one-hour-per-week face-to-face session in which students could ask questions or be given targeted assistance. The CMU statistics course was implemented in this hybrid format alongside traditionalformat versions of the same course at six public university campuses (including two separate courses in two departments on one campus) that agreed to cooperate in a research project utilizing random assignment techniques. Two of these campuses are part of the State University of New York (SUNY); two are part of the University of Maryland; and two are part of the City University of New York (CUNY). The individual campuses involved in this study were, from SUNY, the University at Albany and SUNY Institute of Technology; from the University of 7

8 Maryland, the University of Maryland, Baltimore County and Towson University; and, from CUNY, Baruch College and City College. The seven courses, with their fall 2011 enrollments, are listed in Table 1. The exact research protocol varied by campus in accordance with local policies, practices, and preferences, and we describe these protocols in detail in Bowen et al. (2012). The general procedure followed was: 1) at or before the beginning of the semester, students registered for the introductory statistics course were asked to participate in our study, and modest incentives were offered; 2) students who consented to participate filled out a baseline survey; 3) study participants were randomly assigned to take the class in a traditional or hybrid format; 4) study participants were asked to take the CAOS test of statistical literacy at the beginning of the semester; and 5) at the end of the semester, study participants were asked to take the CAOS test of statistical literacy again, as well as complete another questionnaire. The CAOS test, or Comprehensive Assessment of Outcomes in Statistics, is a 40-item, multiple-choice standardized assessment designed to measure students statistical literacy and reasoning skills (delmas et al., 2007). Administrative data on participating and non-participating students were gathered from the participating campus institutional research offices. The baseline survey administered to students included questions on their background characteristics, such as socioeconomic status, as well as their prior exposure to statistics and the reason for their interest in possibly taking the statistics course in a hybrid format. The end-of-semester survey asked questions about their experiences in the statistics course. Students in study-affiliated sections of the statistics course took a final exam that included a set of items that were identical across all the participating sections at that campus (or, in the case of the campus that had two departments participating in 8

9 the study, all participating sections in that department). The scores of study participants on this common portion of the exam were provided to the research team, along with background administrative data and final course grades of all students (both participants and, for comparison purposes, non-participants) enrolled in the statistics course in the fall 2011 semester. 7 The treatment and control groups are described in Table 2. These data indicate that the randomization worked properly in that traditional- and hybrid-format students in fact have similar characteristics. Two differences are statistically significant at the 10 percent level, which is roughly what we would expect to find by random chance given that there are 22 characteristics examined. A regression of format assignment on all of the variables listed in Table 2 (and course dummy variables) fails to reject the null hypothesis of zero coefficients for all variables (except the course dummies) with p=0.16. A Hotelling test fails to reject the null of no difference in means with p=0.30. Table 2 also shows that the students who participated in our study are a very diverse group. Half of the students come from families with incomes less than $50,000 and half are first-generation college students. Fewer than half are white, and the group is about evenly divided between students with college GPAs above and below 3.0. Most students are of traditional college-going age (younger than 24), are enrolled full-time, and are in their sophomore or junior year. Although the students participating in the study are a diverse group, they are a self-selected population because only students who agreed to be in the study were randomly assigned, and scheduling complications limited the population of participants. Overall, 605 of the 3,045 students enrolled in these statistics courses participated in the study. An even larger sample size would 7 These data are described in detail in Bowen et al. (2012), and additional information including copies of the survey instruments is available at 9

10 have been desirable, but the logistical challenges of scheduling at least two sections (one hybrid section and one traditional section) at the same time, so as to enable students in the study to attend the statistics course regardless of their (randomized) format assignment, restricted our prospective participant pool to the limited number of paired time slots available. Not surprisingly, some students who were able to make the paired time slots elected not to participate in the study. All of these complications notwithstanding, our final sample of 605 students is by no means small it is in fact quite large compared to other research on online learning. 8 The data in Table 3 indicate that the 605 study participants, while not fully representative of all statistics students in any formal sense, have broadly similar characteristics. There are statistically significant differences between study participants and non-participants on several characteristics, but most of the differences are small in magnitude. For example, participants are more likely to be enrolled full-time, but only by a margin of 89 versus 86 percent. Course outcomes are also broadly similar, with participants earning similar grades and being only slightly less likely to complete and pass the course as compared to non-participants. Of course, the population of participants may be more likely to believe that they may benefit from a hybrid model of instruction. If that is the case, and if the hypothetical self-perception is accurate, then the hybrid course effects we estimate would be larger than we would obtain if we were able to randomly assign all students to a format without their consent. In a similar vein, the instructors who volunteered to teach the hybrid sections in this study may be particularly well-suited and motivated to teach in this format. Different results might be obtained if the hybrid sections were taught by instructors less well-suited to this mode of instruction. 8 Of the 45 studies examined in the U.S. Department of Education (2010) meta-analysis, only five had sample sizes of over 400, and of the 50 independent effect sizes the authors abstracted, 32 came from studies with fewer than 100 study participants. 10

11 A notable limitation of these experiments is that although we were successful in randomizing students between treatment and control groups, we could not randomize instructors and thus could not control for differences in instructor quality. Table 4 reports student-weighted instructor and section characteristics by format for study participants. These data are drawn largely from instructor surveys, which were completed by instructors responsible for 90 percent of study participants. Table 4 shows that the hybrid sections were roughly similar in size to the traditional sections, but met for 1.5 hours less face-to-face time each week, on average. There were large differences in instructor characteristics, with hybrid instructors more likely to be employed full-time and to have taught online before but less likely to be tenure-track faculty. Hybrid instructors also had markedly less experience than traditional instructors, but still had 11 years of teaching experience, on average. Some of the differences shown in Table 4 appear to advantage the hybrid sections, whereas others go in the opposite direction. Consequently, it is unclear a priori whether our results will overstate or understate the hybrid effect relative to an experiment that randomized both students and instructors. That depends not only on the balance of instructor characteristics in these experiments but also on the kinds of instructors who would be willing to be randomized to section format in the hypothetical experiment. We briefly return to this issue below and show that controlling for observable characteristics an imperfect solution to this issue does not qualitatively alter the results for three out of four of the learning outcomes we examine. IMPACTS ON LEARNING OUTCOMES Our analysis of the data is straightforward; we compare the outcomes of students randomly assigned to the traditional format to the outcomes of students randomly assigned to the hybrid 11

12 format. In a small number of cases four percent of the 605 students in the study participants attended a different format section than the one to which they were randomly assigned. In order to preserve the randomization procedure, we associated students with the section type to which they were randomly assigned. The intent-to-treat (ITT) estimates that we report can be scaled up to treatment-on-the-treated (TOT) estimates by dividing by 0.04 (i.e. increasing the estimates by about four percent). We do not report TOT estimates because they are so similar to the ITT estimates, because most students took the course in the format to which they were randomly assigned. Specifically, we estimate the following equation: Y ic = β 0 + β 1 Hybrid ic + δ c + ε ic, where Y ic is the outcome of student i in course c, β 0 is a constant, Hybrid ic is a dummy variable indicating whether the student was randomly assigned to the hybrid format (as opposed to the traditional format), δ c is a vector of course-specific dummy variables, and ε ic is the error term. We control for course dummies because students were randomized within courses; these variables also control for unobserved student characteristics that are constant within institutions. However, we obtain similar results when we do not control for course dummies, as would be expected given that the probability of being assigned to the hybrid section was constant across courses (50 percent). The equation is estimated via ordinary least squares (OLS) for continuous outcomes and probit regression for binary outcomes (for the latter, we report marginal effects calculated at the mean of the independent variables). Standard errors are adjusted for clustering by course section in order to capture section-specific shocks to student outcomes (such as the quality of the instructor). 9 9 In some cases, students switched sections over the course of the semester. In these cases, we associated students with their section at the start of the semester if it was available in the administrative data. Students who were 12

13 We first examine the impact of assignment to the hybrid format, relative to the traditional format, in terms of the rate at which students completed and passed the course, their performance on a standardized test of statistics (the CAOS test), and their score on a set of final exam questions that were the same in the two formats. Our main results are reported in Table The only statistically significant difference in learning outcomes between students in the traditional- and hybrid-format sections is the five-percentage-point higher course completion rate among the students assigned to the hybrid format. The difference in pass rates is slightly smaller, at four percentage points, and not statistically significant from zero. (A student can complete the course without passing it by remaining enrolled until the end of the semester but receiving a failing grade.) Hybrid-format students achieved similar scores on the CAOS test and slightly higher scores on the final exam. We obtain similar results with and without including control variables, including race/ethnicity, gender, age, full-time versus part-time enrollment status, class year in college, parental education, language spoken at home, and family income. These controls are not strictly necessary since students were randomly assigned to section format, but we include them in order to increase the precision of our results and to control for any remaining imbalance in observable characteristics. However, we obtain nearly identical results when we do not include these control variables just as we would expect given the apparent success of our random assignment procedure. randomly assigned but never enrolled in the course are grouped as a section within each course for the purpose of computing clustered standard errors. 10 Note that the pass rate in Table 5 cannot be used to calculate the percentage of students who failed the course because the non-passing group includes students who never enrolled or withdrew from the course without receiving a grade. 13

14 It is important to report that our estimated treatment impacts are fairly precisely estimated. We can be quite confident that treatment effects on course completion and pass rates were small or nil, and that the effects on CAOS and final exam scores were not large. For example, the results reported in the bottom panel of Table 5 indicate that we can rule out with 95 percent confidence the possibility that the hybrid format had a negative effect on pass rates of more than 2.4 percentage points. Likewise, we can rule out negative effects on CAOS and final exam scores larger than 1.6 and 2.8 percentage points, respectively. These 95 percent confidence interval lower bounds translate into 0.15 and 0.13 standard deviations (based on the distribution of the control group) for the CAOS and final exam, respectively. In other words, we can confidently rule out the possibility that assignment to the hybrid format had a large negative impact on student outcomes, but we cannot rule out small effects. Some degree of caution is warranted in interpreting the results for the CAOS post-test because the average student s CAOS score only increased by five percentage points over the course of the semester (among students who took both the pre-test and the post-test). This may have resulted in part from some students not taking the CAOS test seriously because, in most cases, it was not part of their grade. 11 We also conducted an analysis in which we grouped the 40 items on the CAOS test into the 20 items on which delmas et al. s (2007) national sample of students exhibited significant growth (over the course of a semester) and the remaining 20 items. We found similar hybrid-format effects for each of the two groups of items. Results that use final exam scores should also be interpreted cautiously given limitations in these exams and their implementation. Some institutions included only a handful of questions that were common across the sections of the course (and we only use data from the common 11 There was a larger increase in CAOS scores at the one campus where the test was part of student s final exam grade. In a study of 763 students at 20 institutions located in 14 states, the average increase was nine percentage points (delmas et al., 2007). 14

15 questions). At one institution, common questions were administered to some students after the end of the semester because the actual final exam only included common questions in two out of six sections (analyses of final exam scores include a dummy variable identifying these students). At another institution, final exam data were not available for the students of two instructors (covering three out of six traditional-format sections). A final potential concern with all of the outcomes besides the CAOS test is that they may have been affected by instructors knowledge of the fact that they were part of a study, and of which students were study participants. For example, they may have used different grading standards that would affect pass rates. However, we think this is unlikely given that we obtain a nearly identical effect using completion rates, which are less likely to be affected by grading standards. The fact that we obtain a similar effect on CAOS and final exam scores increases our confidence that the effect on final exam scores is not biased by differential grading practices (which could not affect the multiple-choice CAOS test). In sum, despite the limitations of the each of the individual outcomes we examined, it is reassuring that the results are consistent across all of these outcomes. Our results are also robust to a variety of alternative methodologies used to analyze the experimental data. These results are reported in Table A1. 12 First, we obtain nearly identical results when we use a linear probability modality (estimated via OLS) instead of a probit model to estimate treatment effects on binary outcomes. Second, we obtain similar results for completion and pass rates when we exclude students who agreed to participate in the study and were randomly assigned but never registered for the course. Third, we also obtain similar results when we exclude the institution where common final exam questions were administered in a follow-up data 12 Tables A1, A2, and A3 appear in Appendix A. All appendices are available at the end of this article as it appears in JPAM online. Go to the publisher s website and use the search engine to locate the article at 15

16 collection, due to a lack of questions on the actual exam that were common across all six study sections at that campus. Fourth, we obtain a larger and noisier estimate of the treatment effect on final exam scores when we standardize these scores separately by institution (instead of using percent correct as the outcome). Fifth, we obtain qualitatively similar results when we control for student scores on the CAOS pre-test (taken by 88 percent of participants), assigning a score of zero to students who did not take the test and identifying them using a dummy variable. Sixth, a limitation of our main results for CAOS post-test and final exam scores is that we only observe these outcomes for students who completed the course and took these exams. This is unlikely to be a significant limitation given that the estimated impact of assignment to the hybrid format on taking the CAOS and final exam is close to zero (not shown). But as an additional check, we assigned students for whom we did not observe a CAOS post-test score their score on the CAOS pre-test in other words, we assumed that their score did not change over the course of the semester. Students who did not take either the pre-test or the post-test were assigned the average pre-test score at their institution. The resulting set of real and imputed post-test scores yielded very similar results to those obtained using only the real data. Finally, we add controls for instructor characteristics, including full-time status, tenuretrack status, years of teaching experience, and whether the instructor has taught online before, as well as section size (from the administrative records). Missing instructor characteristics (due to missing survey data) are assigned a value of zero, and these observations are identified using a dummy variable. Controlling for these characteristics is a crude approach to dealing with our inability to randomly assign instructors to section formats. The estimation of the relationship between these characteristics and student outcomes is likely to be very imprecise given the 16

17 relatively small number of instructors in our data (we obtained survey data from 24 out of 26 instructors), and of course these models will not capture variation in instructor quality that is orthogonal to the measured characteristics. The final row of Table A1 shows that adding these controls leaves the general pattern of results unchanged. The estimated hybrid effects on completion and pass rates change sign, but are less precisely estimated. The effect on CAOS scores is approximately the same; only the effect on final exam scores changes sign and is statistically significant. Given the limitations of this approach to accounting for differences in instructor quality, we place more stock in the lack of an overall shift in results than in a significant change in one point estimate. The lack of differences in mean outcomes between formats could mask differences in the distribution of outcomes. Figure 1 shows that this is not the case for CAOS post-test scores. The distributions of scores for traditional and hybrid format students are largely similar, although scores are slightly more spread-out for hybrid-format students. We obtain a similar finding for final exam scores (not shown). Results broken down by individual institution (Table A2) do not reveal any noteworthy patterns. These results are much noisier because they are based on smaller numbers of students, but they do not indicate that the hybrid format was particularly effective or ineffective at any individual institution with the possible exception of Institution F, where coefficients are positive across all four outcomes, although only statistically significant in the case of one outcome. We also calculated results separately for subgroups of students defined in terms of various characteristics, including race/ethnicity, gender, parents education and income, primary language spoken, CAOS pre-test score, hours worked for pay, and college GPA. We did not find any consistent evidence that the hybrid-format effect varied by any of these characteristics (Table A3). 17

18 The one possible exception is our finding that completion and pass rates were significantly higher in the hybrid course for students with family incomes of at least $50,000 per year, but not for students with family incomes of less than $50,000 per year. However, we hesitate to attach much significance to this result given that we do not find such a clear pattern for our other measure of socioeconomic status (parental education). Given the likelihood of finding spurious effects when a large number of coefficients are estimated (as in Table A3), the most likely conclusion is that there were no groups of students who benefited from or were harmed by the hybrid format consistently across multiple learning outcomes. This conclusion differs noticeably from Figlio, Rush, and Yin s (Forthcoming) finding of negative effects of watching video-taped lectures (relative to live lectures) among Hispanic, male, and lower-achieving students. As discussed earlier, the most important difference between that study and the present one is the very different type of technology evaluated: video-taped lectures as compared to a sophisticated, interactive online course. There are at least three other differences that may also account for the difference in findings. First, the contexts were quite different: an economics course at a highly selective university as opposed to statistics courses at moderately selective universities. Second, Figlio, Rush, and Yin compared an online-only format to a liveonly format (neither had discussion sections), whereas the present study compares a hybrid format to a live-only format (although courses in both studies had web sites for the distribution of course materials). Third, the different formats in Figlio, Rush, and Yin s study were taught by the same instructor, whereas different instructors taught the hybrid and traditional formats in the present study. But taken together, our results and those in Figlio, Rush, and Yin indicate that a more expensive hybrid course may yield better outcomes than simply presenting traditional large lecture courses in an electronic medium, a strategy that universities may pursue as a cost-cutting device. 18

19 In addition to examining learning outcomes, we also asked students how much they liked the course that they took (Table 6). We found that students gave the hybrid format a modestly lower overall rating than the one given by students taking the course in the traditional format (the rating was about 11 percent lower). By similar margins, hybrid students reported feeling that they learned less and that they found the course more difficult. 13 These three differences, though modest in size, were statistically significant at the 10 percent level. But there were no statistically significant differences in students reports of how much the course raised their interest in the subject matter. Finally, we asked students how many hours per week they spent outside of class working on the statistics class. Hybrid-format students reported spending 0.3 hour more each week, on average, than traditional-format students. This difference, which is not statistically significant, implies that, in a course where the traditional section meet for 1.5 hours more per week than the hybrid sections (see Table 4), the average hybrid-format student would spend 1.2 less hours each week in total time devoted to the course, a difference of about 18 percent. This result is consistent with other evidence that ILO-type formats do succeed in achieving the same learning outcomes as traditional-format instruction in less time which potentially has important implications for scheduling and the rate of course completion (Lovett, Meyer, and Thille, 2008). In sum, our results indicate that hybrid-format students took about one-fifth less time to achieve essentially the same learning outcomes as traditional-format students. The three main limitations of this analysis are: 1) the fact that we were not able to randomly assign instructors to 13 Students responses to the open-ended questions on the end-of-semester surveys indicate that many students in the hybrid format would have liked more face-to-face time with the instructor than one hour each week; others felt that the instructor could have better used the face-to-face time to make the weekly sessions more structured or been more helpful in explaining the material and going over concepts students did not understand. A number of students in the hybrid course also indicated they would have benefited from more practice problems or examples, and many were frustrated by the difficulty of checkpoint assessments in the course and by problems they encountered using the statistical software packages to complete assignments. 19

20 section formats which would have been difficult, if not impossible, to do; 2) the limits to external validity that result from the need to recruit students willing to be randomized; and 3) the limitations of the CMU prototype of an ILO course. Despite these limitations, these results represent the first rigorous assessment of the relative efficacy of technology-enhanced learning (ILO-style hybrid instruction) compared to the traditional mode of instruction in large introductory courses on multiple public university campuses. At a minimum, this study supports a no-harm-done conclusion regarding one current prototype of an ILO system. But there is, without doubt, much more research that can and should be carried out. Future experimental studies should examine courses in subjects other than statistics. ILO courses may have more potential in subject areas where there is usually a right answer, such as math and science, but there is little evidence on this question. Future research should also examine the intersection between the instructor and the delivery method. Having the same instructor teach in both formats would allow for an evaluation that holds constant the quality of the instructor, and if repeated over time would produce evidence as to whether some instructors are more effective when teaching in a particular format. COSTS AND POTENTIAL SAVINGS The experimental data on learning outcomes results described above show that a relatively sophisticated prototype hybrid learning system did not lead to a significant increase in outputs (student learning), but could potentially increase productivity by using fewer inputs. Costs are difficult to measure at the course level, which is a likely reason why so few prior studies have paid much attention to costs. 14 The key problem is that contemporaneous comparisons can be near- 14 Carol Twigg s work with the National Center for Academic Transformation (NCAT) project is a notable exception (see the NCAT website at 20

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